Tensor Flow-powered Spam Email Filtering: An Evaluation of Performance and Robustness

Abstract

Spam emails continue to pose significant challenges in email communication, requiring robust and adaptive filtering mechanisms to safeguard users and organizations. Leveraging the capabilities of machine learning and deep learning, this paper presents an evaluation of a spam email filtering system powered by Tensor Flow. The system's architecture is designed to utilize deep neural networks for feature extraction and classification, enabling flexibility and scalability in handling diverse spam tactics. We assess the system's performance in accurately distinguishing between spam and legitimate emails, evaluating metrics such as precision, recall, and F1-score. Additionally, we analyze the system's resilience against adversarial attacks and its ability to adapt to evolving spam techniques. Comparative analysis with traditional spam filtering techniques highlights the superiority of deep learning-based approaches. Practical considerations such as computational efficiency and scalability are also addressed, ensuring real-time responsiveness in processing vast volumes of emails. Through comprehensive experimentation and benchmarking, this paper contributes to the advancement of spam email filtering, guiding the development of more effective and efficient solutions for enhancing email security and user experience.